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全球能源互联网:英文版 2023年第5期6卷 554-566页

作者： Min Ding Zili Tao Bo Hu Meng Ye Yingxiong Ou Ryuichi Yokoyama School of Automation China University of GeosciencesWuhan 430074P.R.China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074P.R.China Global Energy Interconnection Group Co. LtdSTATE GRID Corporation of ChinaBeijing 100032P.R.China School of Environment and Energy Engineering Waseda UniversityTokyo169-8050Japan

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions.Finally,the effectiveness of this method is verified via Simulink simulation.

关键词： Maximum wind power tracking Fuzzy control neural network

A neural network-based production process modeling and variable importance analysis approach in corn to sugar factory

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Frontiers of Chemical Science and Engineering 2023年第3期17卷 358-371页

作者： Yi Tong Mou Shu Mingxin Li Yingwei Liu Ran Tao Congcong Zhou You Zhao Guoxing Zhao Yi Li Yachao Dong Lei Zhang Linlin Liu Jian Du COFCO Biotechnology Co. Ltd.Beijing 100005China COFCO Nutrition and Health Research Institute Co. Ltd.Beijing 102209China Institute of Process Systems Engineering School of Chemical EngineeringDalian University of TechnologyDalian 116024China

Corn to sugar process has long faced the risks of high energy consumption and thin profits.However,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes.Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions.In this paper,a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes,which contains data preprocessing,dimensionality reduction,multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method.In the established model,dextrose equivalent value is selected as the output,and 654 sites from the DCS system are selected as the inputs.LASSO analysis is first applied to reduce the data dimension to 155,then the inputs are dimensionalized to 50 by means of genetic algorithm optimization.Ultimately,variable importance analysis is carried out by the extended weight connection method,and 20 of the most important sites are selected for each neural network.The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%,which have a better prediction result than other models,and the 20 most important sites selected have better explicable performance.The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.

关键词： big data corn to sugar factory neural network variable importance analysis

Atmospheric correction for HY-1C CZI images using neural network in western Pacific region

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Geo-Spatial Information Science 2022年第3期25卷 476-488页

作者： Jilin Men Jianqiang Liu Guangping Xia Tong Yue Ruqing Tong Liqiao Tian Kohei Arai Linyu Wang State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Ministry of Natural Resource of the People’s Republic of China National Satellite Ocean Application ServiceBeijingChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Department of Information Science Saga UniversitySagaJapan Department of Urban Development Wuhan Land Containment and Reserve CenterWuhanChina

With a spatial resolution of 50 m,a revisit time of three days,and a swath of 950 km,the coastal zone imager(CZI)offers great potential in monitoring coastal zone dynamics.Accurate atmo-spheric correction(AC)is needed to exploit the potential of quantitative ocean color inversion.However,due to the band setting of CZI,the AC over coastal waters in the western Pacific region with complex optical properties cannot be realized easily.This research introduces a novel neural network(NN)AC algorithm for CZI data over coastal waters.Total 100,000 match-ups of HY-1 C CZI-observed reflectance at the top-of-atmosphere and Operational Land Imager(OLI)-retrieved high-quality remote sensing reflectance(Rrs)at the CZI bands are built to train the NN model.These reflectance data are obtained from the standard AC algorithm in the SeaDAS.Results indicate that the distributions of the CZI retrieved Rrs were consistent with the quasi-synchronous OLI data,but the spatial information from the CZI is more detailed.Then,the accuracy of the CZI data for AC is evaluated using the multi-source in-situ data.Results further show that the NN-AC can successfully retrieve Rrs for CZI and the coefficients of determination in the blue,green,red,and near-infrared bands were 0.70,0.77,0.76,and 0.67,respectively.The NN algorithm does not depend on shortwave-infrared bands and runs very fast once properly trained.

关键词： HaiYang-1C coastal zone imager(HY-1C CZI) atmospheric correction neural network coastal water remote sensing

Adaptive neural network based boundary control of a flexible marine riser system with output constraints

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Frontiers of Information Technology & Electronic Engineering 2022年第8期23卷 1229-1238页

作者： Chuyang YU Xuyang LOU Yifei MA Qian YE Jinqi ZHANG Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxi 214122China College of Internet of Things Technology Wuxi Institute of TechnologyWuxi 214122China Wuxi Good Cloud IoT Technology Co. Ltd.Wuxi 214196China

In this study,we develop an adaptive neural network based boundary control method for a flexible marine riser system with unknown nonlinear disturbances and output constraints to suppress vibrations.We begin with describing the dynamic behavior of the riser system using a distributed parameter system with partial differential equations.To compensate for the effect of nonlinear disturbances,we construct a neural network based boundary controller using a radial basis neural network to reduce vibrations.Under the proposed boundary controller,the state of the riser is guaranteed to be uniformly bounded based on the Lyapunov method.The proposed methodology provides a way to integrate neural networks into boundary control for other flexible robotic manipulator systems.Finally,numerical simulations are given to demonstrate the effectiveness of the proposed control method.

关键词： Marine riser system Partial differential equation neural network Output constraint Boundary control Unknown disturbance

Characterizing human driver characteristics using an artificial neural network and a theoretical model

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Control Theory and Technology 2022年第2期20卷 263-278页

作者： Sangmyoeng Kim Takeshi Miyamoto Tatsuya Kuboyama Yasuo Moriyoshi Graduate School of Engineering Chiba University.1-33 Yayoicho InagekuChiba 2638522Japan

Human drivers seem to have different characteristics,so different drivers often yield different results from the same driving mode tests with identical vehicles and same chassis dynamometer.However,drivers with different experiences often yield similar results under the same driving conditions.If the features of human drivers are known,the control inputs to each driver,including warnings,will be customized to optimize each man–machine vehicle system.Therefore,it is crucial to determine how to characterize human drivers quantitatively.This study proposes a method to estimate the parameters of a theoretical model of human drivers.The method uses an artificial neural network(ANN)model and a numerical procedure to interpret the identified ANN models theoretically.Our approach involves the following process.First,we specify each ANN driver model through chassis dynamometer tests performed by each human driver and vehicle.Subsequently,we obtain the parameters of a theoretical driver model using the ANN model for the corresponding driver.Specifically,we simulate the driver’s behaviors using the identified ANN models with controlled inputs.Finally,we estimate the theoretical driver model parameters using the numerical simulation results.A proportional-integral-differential(PID)control model is used as the theoretical model.The results of the parameter estimation indicate that the PID driver model parameter combination can characterize human drivers.Moreover,the results suggest that vehicular factors influence the parameter combinations of human drivers.

关键词： neural network Identification Driver model Chassis dynamometer Driver characteristics

Electron tomography for sintered ceramic materials by a neural network algebraic reconstruction technique

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Journal of Materials Science & Technology 2022年第5期100卷 75-81页

作者： R.H.Shen Y.T.He W.Q.Ming Y.Zhang X.D.Xu J.H.Chen Centre for High Resolution Electron Microscopy College of Materials Science and EngineeringHunan UniversityChangsha 410082China

The missing wedge effect in electron tomography introduces various types of artifacts in the tomograms and lowers the reconstruction resolution and quality.The artifacts produced in tomographic reconstruction of bulk materials can be very severe,particularly for sintered bulk ceramic materials in which there are often nano-pores or pore-like microstructure features.Here,we report a neural network algebraic reconstruction algorithm with no prior knowledge to perform electron tomography for a sintered SiC material with nano carbon zones.The results show that the proposed algorithm has a great suppressive effect on the missing wedge artifacts and a high tolerance for noise.The information in the missing wedge can be partly recovered by this technique.Thus,both the shape of the bulk SiC specimen and its irregular inner pore-like features are correctly retrieved in the obtained 3D image.Our study shows the effectiveness of the neural network algorithm for improving the reconstruction accuracy of electron tomography,in order to reveal sophisticated 3D microstructures generally existing in sintered ceramic materials.

关键词： neural network Missing wedge Electron tomography Sintered ceramics

CAN:Effective Cross Features by Global Attention Mechanism and neural network for Ad Click Prediction

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Tsinghua Science and Technology 2022年第1期27卷 186-195页

作者： Wenjie Cai Yufeng Wang Jianhua Ma Qun Jin Nanjing University of Posts and Telecommunications(NJUPT) Nanjing210003China Digital Media Department in the Faculty of Computer and Information Sciences Hosei UniversityTokyo 163-8001Japan networked Information Systems Laboratory Department of Human Informatics and Cognitive SciencesWaseda UniversityTokyo 163-8001Japan

Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent years.One of the hot topics in this area is the construction of feature interactions to facilitate accurate prediction.Factorization machine provides second-order feature interactions by linearly multiplying hidden feature factors.However,real-world data present a complex and nonlinear structure.Hence,second-order feature interactions are unable to represent cross information adequately.This drawback has been addressed using deep neural networks(DNNs),which enable high-order nonlinear feature interactions.However,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original features.In this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN models.The attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature interactions.The experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.

关键词： click-through rate prediction global attention mechanism feature interaction neural network

Predictions of nuclear charge radii based on the convolutional neural network

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Nuclear Science and Techniques 2023年第10期34卷 83-90页

作者： Ying-Yu Cao Jian-You Guo Bo Zhou Key Laboratory of Nuclear Physics and Ion‑beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China School of Physics and Optoelectronics Engineering Anhui UniversityHefei 230601China Shanghai Research Center for Theoretical Nuclear Physics NSFC and Fudan UniversityShanghai 200438China

In this study, we developed a neural network that incorporates a fully connected layer with a convolutional layer to predict the nuclear charge radii based on the relationships between four local nuclear charge radii. The convolutional neural network(CNN) combines the isospin and pairing effects to describe the charge radii of nuclei with A ≥ 39 and Z ≥ 20. The developed neural network achieved a root mean square(RMS) deviation of 0.0195 fm for a dataset with 928 nuclei. Specifically, the CNN reproduced the trend of the inverted parabolic behavior and odd–even staggering observed in the calcium isotopic chain, demonstrating reliable predictive capability.

关键词： Nuclear charge radii Machine learning neural network

Digraph states and their neural network representations

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Chinese Physics B 2022年第6期31卷 183-191页

作者：杨莹曹怀信 School of Mathematics and Information Technology Yuncheng UniversityYuncheng 044000China School of Mathematics and Statistics Shaanxi Normal UniversityXi’an 710119China

With the rapid development of machine learning,artificial neural networks provide a powerful tool to represent or approximate many-body quantum states.It was proved that every graph state can be generated by a neural network.Here,we introduce digraph states and explore their neural network representations(NNRs).Based on some discussions about digraph states and neural network quantum states(NNQSs),we construct explicitly an NNR for any digraph state,implying every digraph state is an NNQS.The obtained results will provide a theoretical foundation for solving the quantum manybody problem with machine learning method whenever the wave-function is known as an unknown digraph state or it can be approximated by digraph states.

关键词： digraph state neural network quantum state representation

TBM performance prediction using LSTM-based hybrid neural network model:Case study of Baimang River tunnel project in Shenzhen,China

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Underground Space 2023年第4期11卷 130-152页

作者： Qihang Xu Xin Huang Baogang Zhang Zixin Zhang Junhua Wang Shuaifeng Wang Department of Geotechnical Engineering College of Civil EngineeringTongji UniversityShanghai 200092China State Key Laboratory of Geotechnical and Underground Engineering Tongji UniversityShanghai 200092China China Construction Fourth Engineering Division Corp.LTD Guangzhou 510665China

Accurately predicting tunnel boring machine(TBM)performance is beneficial for excavation efficiency enhancement and risk miti-gation of TBM tunneling.In this paper,we develop a long short-term memory(LSTM)based hybrid intelligent model to predict two key TBM performance parameters(advance rate and cutterhead torque).The model combines the LSTM,BN,Dropout and Dense layers to process the raw data and improve the fitting quality.The features,including the ground formation properties,tunnel route cur-vature,tunnel location and TBM operational parameters,are divided into historical/real-time time-varying parameters,time-invariant parameters and historical/real-time output prediction data.The effectiveness of the proposed model is verified based on a large moni-toring database of the Baimang River Tunnel Project in Shenzhen,south China.We then discuss the influence of the prediction mode,neural network structure and time division interval length of historical data on the prediction accuracy.The significance evaluation of input features shows that the historical output prediction has the largest influence on the prediction accuracy,and the influence of ground properties is secondary.It is also found that the correlations between input features and the output prediction are coincident with their interrelationships with the ground properties and ease of TBM excavation.Finally,it is found that the prediction results are most affected by the total propulsion force followed by the rotation speed of the cutterhead.The established model can provide useful guidance for construction personnel to roughly grasp the possible TBM status from the prediction results when adjusting the operational parameters.

关键词： TBM performance LSTM Deep learning neural network Advance rate Cutterhead torque